To tackle the problem, this paper proposes a novel perspective by observing the fact that the pixel-level labels generated by CAM contain a large number of noisy labels (compared with ground truth human annotations).
We believe that semi-supervised semantic segmentation can be formulated as a problem of learning with pixel-level label noise. However, existing approaches of learning from noisy labels mainly aim at image classification tasks, such as CIFAR-100 [1]}, Clothing-1M [2]}, how to learn with pixel-level noisy labels has not been explored. Compared with image-level noisy labels, the critical issue of pixel-level noisy labels is to model the relations between pixel labels in one image. Therefore, we propose a graph based label noise detection and correction framework to capture the relations between neighboring pixels and further use these relations to correct the noisy labels.
